Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[3]:
<matplotlib.image.AxesImage at 0x7f99d70d98d0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [4]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[4]:
<matplotlib.image.AxesImage at 0x7f99d704fe80>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [5]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
TensorFlow Version: 1.8.0
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [6]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name="input_real")
    inputs_z    = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    lr          = tf.placeholder(tf.float32)
    return inputs_real, inputs_z, lr

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [7]:
def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        
        x1 = tf.layers.conv2d(images, filters=64, 
                              kernel_size=5, strides=(2, 2), padding='same')
        bn1 = tf.layers.batch_normalization(x1, training=True)
        lrelu1 = tf.maximum(alpha*bn1, bn1)
        
        x2 = tf.layers.conv2d(lrelu1, filters=128,
                              kernel_size=5, strides=(2, 2), padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        lrelu2 = tf.maximum(alpha*bn2, bn2)
        
        x3 = tf.layers.conv2d(lrelu2, filters=256,
                             kernel_size=5, strides=(2, 2), padding='same')
        bn3 =  tf.layers.batch_normalization(x3, training=True)
        lrelu3 = tf.maximum(alpha*bn3, bn3)
        
        flat = tf.reshape(lrelu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat,1)
        out = tf.sigmoid(logits)
        
        return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse=not is_train):
        x1 = tf.layers.dense(z, 3*3*256)
        
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 3, 3, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 3x3x256

        x2 = tf.layers.conv2d_transpose(x1, 256, 3, strides=2, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 7x7x256

        x3 = tf.layers.conv2d_transpose(x2, 128, 3, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 14x14x128
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 3, strides=2, padding='same')
        # 28x28x out_channel_dim
        
        # scale tanh -1,1 to -0.5,0.5 range
        out = tf.tanh(logits)/2
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, alpha=alpha)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, 
                                                labels=tf.ones_like(d_logits_real) * 0.9))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                labels=tf.zeros_like(d_logits_fake)))
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                labels=tf.ones_like(d_logits_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimizer
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt  


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model

    # model_inputs
    data_len, img_w, img_h, img_c = data_shape
    input_real, input_z, lr = model_inputs(img_w, img_h, img_c, z_dim)
    
    # model_loss graphs
    d_loss, g_loss = model_loss(input_real, input_z, img_c)
    
    # model_optimizer
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    saver = tf.train.Saver()

    samples, losses = [], []
    steps = 0
    
    print_every=100
    show_every=100
    figsize=(16,16)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for e in range(epochs):
            for x in get_batches(batch_size):
                steps += 1

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _, train_loss_d = sess.run([d_opt, d_loss], feed_dict={input_z: batch_z, input_real: x, lr: learning_rate})
                _, train_loss_g = sess.run([g_opt, g_loss], feed_dict={input_z: batch_z, input_real: x, lr: learning_rate})
                
                if steps % print_every == 0:
                    print("Epoch {}/{}, step {}..".format(e+1, epochs, steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
    
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, 36, input_z, img_c, data_image_mode)

        saver.save(sess, './checkpoints/dcgan_mnist.ckpt')
    
    return losses, samples

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [13]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2, step 100.. Discriminator Loss: 0.5459... Generator Loss: 4.0310
Epoch 1/2, step 200.. Discriminator Loss: 0.5919... Generator Loss: 3.8155
Epoch 1/2, step 300.. Discriminator Loss: 0.7032... Generator Loss: 2.0915
Epoch 1/2, step 400.. Discriminator Loss: 0.5087... Generator Loss: 3.3995
Epoch 1/2, step 500.. Discriminator Loss: 0.6815... Generator Loss: 1.9900
Epoch 1/2, step 600.. Discriminator Loss: 0.4890... Generator Loss: 3.4024
Epoch 1/2, step 700.. Discriminator Loss: 0.6809... Generator Loss: 4.4713
Epoch 1/2, step 800.. Discriminator Loss: 0.6323... Generator Loss: 4.0548
Epoch 1/2, step 900.. Discriminator Loss: 0.5388... Generator Loss: 3.1590
Epoch 1/2, step 1000.. Discriminator Loss: 0.6423... Generator Loss: 2.1912
Epoch 1/2, step 1100.. Discriminator Loss: 0.4585... Generator Loss: 2.6890
Epoch 1/2, step 1200.. Discriminator Loss: 0.5238... Generator Loss: 3.4413
Epoch 1/2, step 1300.. Discriminator Loss: 0.5073... Generator Loss: 2.9947
Epoch 1/2, step 1400.. Discriminator Loss: 1.3521... Generator Loss: 0.3740
Epoch 1/2, step 1500.. Discriminator Loss: 0.4814... Generator Loss: 3.1477
Epoch 1/2, step 1600.. Discriminator Loss: 0.5653... Generator Loss: 3.3541
Epoch 1/2, step 1700.. Discriminator Loss: 0.4571... Generator Loss: 3.7090
Epoch 1/2, step 1800.. Discriminator Loss: 0.4220... Generator Loss: 4.1811
Epoch 2/2, step 1900.. Discriminator Loss: 0.7244... Generator Loss: 3.6046
Epoch 2/2, step 2000.. Discriminator Loss: 0.4852... Generator Loss: 3.4079
Epoch 2/2, step 2100.. Discriminator Loss: 0.4114... Generator Loss: 3.4136
Epoch 2/2, step 2200.. Discriminator Loss: 0.7978... Generator Loss: 1.5353
Epoch 2/2, step 2300.. Discriminator Loss: 0.6455... Generator Loss: 1.6296
Epoch 2/2, step 2400.. Discriminator Loss: 0.5128... Generator Loss: 3.0160
Epoch 2/2, step 2500.. Discriminator Loss: 0.4746... Generator Loss: 3.6513
Epoch 2/2, step 2600.. Discriminator Loss: 0.5819... Generator Loss: 4.1898
Epoch 2/2, step 2700.. Discriminator Loss: 0.8321... Generator Loss: 3.6644
Epoch 2/2, step 2800.. Discriminator Loss: 0.7093... Generator Loss: 1.7983
Epoch 2/2, step 2900.. Discriminator Loss: 0.4698... Generator Loss: 2.9018
Epoch 2/2, step 3000.. Discriminator Loss: 0.4097... Generator Loss: 4.1462
Epoch 2/2, step 3100.. Discriminator Loss: 0.6528... Generator Loss: 3.7087
Epoch 2/2, step 3200.. Discriminator Loss: 0.8742... Generator Loss: 4.9816
Epoch 2/2, step 3300.. Discriminator Loss: 0.5732... Generator Loss: 3.1846
Epoch 2/2, step 3400.. Discriminator Loss: 0.4812... Generator Loss: 4.0389
Epoch 2/2, step 3500.. Discriminator Loss: 0.4598... Generator Loss: 3.8872
Epoch 2/2, step 3600.. Discriminator Loss: 0.4362... Generator Loss: 3.5188
Epoch 2/2, step 3700.. Discriminator Loss: 0.4225... Generator Loss: 3.0994

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [14]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1, step 100.. Discriminator Loss: 0.4603... Generator Loss: 4.9430
Epoch 1/1, step 200.. Discriminator Loss: 0.5651... Generator Loss: 5.8953
Epoch 1/1, step 300.. Discriminator Loss: 0.7010... Generator Loss: 4.0131
Epoch 1/1, step 400.. Discriminator Loss: 0.7533... Generator Loss: 2.6827
Epoch 1/1, step 500.. Discriminator Loss: 0.5493... Generator Loss: 3.3111
Epoch 1/1, step 600.. Discriminator Loss: 0.8147... Generator Loss: 3.5573
Epoch 1/1, step 700.. Discriminator Loss: 0.8461... Generator Loss: 2.3277
Epoch 1/1, step 800.. Discriminator Loss: 0.8019... Generator Loss: 3.0138
Epoch 1/1, step 900.. Discriminator Loss: 0.8429... Generator Loss: 3.3385
Epoch 1/1, step 1000.. Discriminator Loss: 1.0363... Generator Loss: 4.0722
Epoch 1/1, step 1100.. Discriminator Loss: 0.8807... Generator Loss: 3.6186
Epoch 1/1, step 1200.. Discriminator Loss: 0.7457... Generator Loss: 3.2632
Epoch 1/1, step 1300.. Discriminator Loss: 0.8477... Generator Loss: 3.1942
Epoch 1/1, step 1400.. Discriminator Loss: 0.8976... Generator Loss: 2.7873
Epoch 1/1, step 1500.. Discriminator Loss: 0.8803... Generator Loss: 1.8690
Epoch 1/1, step 1600.. Discriminator Loss: 0.7931... Generator Loss: 2.4063
Epoch 1/1, step 1700.. Discriminator Loss: 0.8573... Generator Loss: 2.9106
Epoch 1/1, step 1800.. Discriminator Loss: 0.8571... Generator Loss: 1.9383
Epoch 1/1, step 1900.. Discriminator Loss: 0.9575... Generator Loss: 1.2950
Epoch 1/1, step 2000.. Discriminator Loss: 0.7440... Generator Loss: 1.9483
Epoch 1/1, step 2100.. Discriminator Loss: 0.9791... Generator Loss: 1.5566
Epoch 1/1, step 2200.. Discriminator Loss: 0.7024... Generator Loss: 2.0261
Epoch 1/1, step 2300.. Discriminator Loss: 0.9447... Generator Loss: 2.9706
Epoch 1/1, step 2400.. Discriminator Loss: 0.5645... Generator Loss: 3.3269
Epoch 1/1, step 2500.. Discriminator Loss: 0.8286... Generator Loss: 1.9977
Epoch 1/1, step 2600.. Discriminator Loss: 0.8320... Generator Loss: 2.7045
Epoch 1/1, step 2700.. Discriminator Loss: 0.7995... Generator Loss: 2.1160
Epoch 1/1, step 2800.. Discriminator Loss: 0.7987... Generator Loss: 2.0531
Epoch 1/1, step 2900.. Discriminator Loss: 1.0134... Generator Loss: 1.9123
Epoch 1/1, step 3000.. Discriminator Loss: 0.7592... Generator Loss: 2.6900
Epoch 1/1, step 3100.. Discriminator Loss: 0.9990... Generator Loss: 1.0631
Epoch 1/1, step 3200.. Discriminator Loss: 1.3517... Generator Loss: 1.1975
Epoch 1/1, step 3300.. Discriminator Loss: 0.6761... Generator Loss: 2.6612
Epoch 1/1, step 3400.. Discriminator Loss: 0.8169... Generator Loss: 2.0227
Epoch 1/1, step 3500.. Discriminator Loss: 0.8593... Generator Loss: 2.1612
Epoch 1/1, step 3600.. Discriminator Loss: 0.6990... Generator Loss: 3.8445
Epoch 1/1, step 3700.. Discriminator Loss: 0.7160... Generator Loss: 2.6965
Epoch 1/1, step 3800.. Discriminator Loss: 0.7088... Generator Loss: 2.6416
Epoch 1/1, step 3900.. Discriminator Loss: 0.8733... Generator Loss: 1.5726
Epoch 1/1, step 4000.. Discriminator Loss: 0.7681... Generator Loss: 3.3260
Epoch 1/1, step 4100.. Discriminator Loss: 0.9479... Generator Loss: 1.2891
Epoch 1/1, step 4200.. Discriminator Loss: 0.6895... Generator Loss: 2.2013
Epoch 1/1, step 4300.. Discriminator Loss: 0.8139... Generator Loss: 3.0471
Epoch 1/1, step 4400.. Discriminator Loss: 0.8032... Generator Loss: 3.4086
Epoch 1/1, step 4500.. Discriminator Loss: 0.8323... Generator Loss: 2.4206
Epoch 1/1, step 4600.. Discriminator Loss: 0.6117... Generator Loss: 2.6193
Epoch 1/1, step 4700.. Discriminator Loss: 1.0402... Generator Loss: 1.6015
Epoch 1/1, step 4800.. Discriminator Loss: 1.1521... Generator Loss: 0.8046
Epoch 1/1, step 4900.. Discriminator Loss: 0.7437... Generator Loss: 1.9501
Epoch 1/1, step 5000.. Discriminator Loss: 0.9114... Generator Loss: 3.7677
Epoch 1/1, step 5100.. Discriminator Loss: 0.7339... Generator Loss: 2.5929
Epoch 1/1, step 5200.. Discriminator Loss: 0.7915... Generator Loss: 3.5156
Epoch 1/1, step 5300.. Discriminator Loss: 0.8788... Generator Loss: 3.1291
Epoch 1/1, step 5400.. Discriminator Loss: 1.0845... Generator Loss: 2.9875
Epoch 1/1, step 5500.. Discriminator Loss: 0.6856... Generator Loss: 3.6188
Epoch 1/1, step 5600.. Discriminator Loss: 0.9410... Generator Loss: 2.2231
Epoch 1/1, step 5700.. Discriminator Loss: 0.8094... Generator Loss: 4.0449
Epoch 1/1, step 5800.. Discriminator Loss: 0.6879... Generator Loss: 2.1104
Epoch 1/1, step 5900.. Discriminator Loss: 0.8315... Generator Loss: 2.1978
Epoch 1/1, step 6000.. Discriminator Loss: 0.8927... Generator Loss: 3.1565
Epoch 1/1, step 6100.. Discriminator Loss: 0.6692... Generator Loss: 2.2987
Epoch 1/1, step 6200.. Discriminator Loss: 0.8196... Generator Loss: 3.9492
Epoch 1/1, step 6300.. Discriminator Loss: 0.9308... Generator Loss: 1.8723

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.